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    • 1. 发明申请
    • Peptides for inhibiting insects
    • 用于抑制昆虫的肽
    • US20050283857A1
    • 2005-12-22
    • US11040472
    • 2005-01-21
    • Michael AdangGang HuaJiang ChenMohd Amir Abdullah
    • Michael AdangGang HuaJiang ChenMohd Amir Abdullah
    • A01N37/18A01N63/02C07K14/435C07K14/705C12N15/82
    • C12N15/8286A01N63/02C07K14/705Y02A40/162A01N2300/00
    • The subject invention pertains to the use of peptide fragments of cadherins (including cadherin-like proteins). The subject invention includes a cell (and use thereof) comprising a polynucleotide that expresses the peptide fragment. The subject invention includes methods of feeding the peptides to insects. In preferred embodiments, the peptides are fed to target insects together with one or more insecticidal proteins, preferably (but not limited to) B.t. Cry proteins. When used in this manner, the peptide fragment can not only enhance the apparent toxin activity of the Cry protein against the insect species that was the source of the receptor but also against other insect species. Preferably, the cadherin is a Bacillus thuringiensis (B.t.) insecticidal crystal protein (Cry) toxin receptor. Preferably, the peptide fragment is a binding domain of the receptor. In some preferred embodiments, the peptide is the binding domain nearest to the membrane proximal ectodomain. Corresponding domains are identifiable in a variety of B.t. toxin receptors.
    • 本发明涉及钙粘蛋白(包括钙粘蛋白样蛋白)的肽片段的用途。 本发明包括含有表达肽片段的多核苷酸的细胞(及其用途)。 本发明包括将肽喂入昆虫的方法。 在优选的实施方案中,将肽与一种或多种杀虫蛋白一起进食至靶昆虫,优选(但不限于)B.t. 哭泣蛋白质 当以这种方式使用时,肽片段不仅可以增强Cry蛋白对作为受体来源的昆虫物种的表观毒素活性,而且还可以抵抗其他昆虫物种。 优选地,钙粘蛋白是苏云金芽孢杆菌(B.t.)杀虫晶体蛋白(Cry)毒素受体。 优选地,肽片段是受体的结合结构域。 在一些优选的实施方案中,肽是最接近膜近端胞外域的结合结构域。 相应的域可以在各种B.t. 毒素受体。
    • 2. 发明授权
    • Method for inhibiting insects with an insect cadherin ectodomain
    • 用昆虫钙粘蛋白胞外域抑制昆虫的方法
    • US07396813B2
    • 2008-07-08
    • US11040472
    • 2005-01-21
    • Michael J. AdangGang HuaJiang ChenMohd Amir Fursan Abdullah
    • Michael J. AdangGang HuaJiang ChenMohd Amir Fursan Abdullah
    • A01N37/18C12N15/82
    • C12N15/8286A01N63/02C07K14/705Y02A40/162A01N2300/00
    • The subject invention pertains to the use of peptide fragments of cadherins (including cadherin-like proteins). The subject invention includes a cell (and use thereof) comprising a polynucleotide that expresses the peptide fragment. The subject invention includes methods of feeding the peptides to insects. In preferred embodiments, the peptides are fed to target insects together with one or more insecticidal proteins, preferably (but not limited to) B.t. Cry proteins. When used in this manner, the peptide fragment can not only enhance the apparent toxin activity of the Cry protein against the insect species that was the source of the receptor but also against other insect species. Preferably, the cadherin is a Bacillus thuringiensis (B.t.) insecticidal crystal protein (Cry) toxin receptor. Preferably, the peptide fragment is a binding domain of the receptor. In some preferred embodiments, the peptide is the binding domain nearest to the membrane proximal ectodomain. Corresponding domains are identifiable in a variety of B.t. toxin receptors.
    • 本发明涉及钙粘蛋白(包括钙粘蛋白样蛋白)的肽片段的用途。 本发明包括含有表达肽片段的多核苷酸的细胞(及其用途)。 本发明包括将肽喂入昆虫的方法。 在优选的实施方案中,将肽与一种或多种杀虫蛋白一起进食至靶昆虫,优选(但不限于)B.t. 哭泣蛋白质 当以这种方式使用时,肽片段不仅可以增强Cry蛋白对作为受体来源的昆虫物种的表观毒素活性,而且还可以抵抗其他昆虫物种。 优选地,钙粘蛋白是苏云金芽孢杆菌(B.t.)杀虫晶体蛋白(Cry)毒素受体。 优选地,肽片段是受体的结合结构域。 在一些优选的实施方案中,肽是最接近膜近端胞外域的结合结构域。 相应的域可以在各种B.t. 毒素受体。
    • 3. 发明授权
    • Image classification
    • 图像分类
    • US08891861B2
    • 2014-11-18
    • US13371719
    • 2012-02-13
    • Gang HuaPaul Viola
    • Gang HuaPaul Viola
    • G06K9/62G06F17/30G06K9/00G06K9/46
    • G06F17/3025G06F17/30262G06K9/00664G06K9/4642G06K9/4652G06K9/6256G06K9/6285
    • Images are classified as photos (e.g., natural photographs) or graphics (e.g., cartoons, synthetically generated images), such that when searched (online) with a filter, an image database returns images corresponding to the filter criteria (e.g., either photos or graphics will be returned). A set of image statistics pertaining to various visual cues (e.g., color, texture, shape) are identified in classifying the images. These image statistics, combined with pre-tagged image metadata defining an image as either a graphic or a photo, may be used to train a boosting decision tree. The trained boosting decision tree may be used to classify additional images as graphics or photos based on image statistics determined for the additional images.
    • 图像被分类为照片(例如,自然照片)或图形(例如,漫画,综合生成的图像)​​,使得当用过滤器搜索(在线)时,图像数据库返回与过滤标准相对应的图像(例如,照片或 图形将被返回)。 在对图像进行分类时,识别关于各种视觉提示(例如,颜色,纹理,形状)的一组图像统计信息。 这些图像统计信息与将图像定义为图形或照片的预先标记的图像元数据可以用于训练增强决策树。 经训练的增强决策树可以用于基于为附加图像确定的图像统计来将附加图像分类为图形或照片。
    • 4. 发明授权
    • Recognition of faces using prior behavior
    • 使用先前行为识别面部
    • US08644563B2
    • 2014-02-04
    • US12637494
    • 2009-12-14
    • Amir AkbarzadehGang Hua
    • Amir AkbarzadehGang Hua
    • G06K9/00
    • G06K9/00288G06K9/00677G06K9/6212
    • Face recognition may be performed using a combination of visual analysis and social context. In one example, a web site such as a social networking site or photo-sharing site allows users to upload photos, and allows faces that appear in the photo to be tagged with users' names. When user A uploads a new photo, two analyses may be performed. First, a face in the photo is compared with known faces of users to determine similarity. Second, it is determined which other users user A frequently uploads photos of. Two probability distributions are created. One distribution assigns high probabilities to users whose photos are similar to the new photo. The other assigns high probabilities to users who frequently appear in photos uploaded by user A. These probability distributions are combined, and the person in the photo is identified as being the person with the highest probability.
    • 可以使用视觉分析和社会语境的组合来执行面部识别。 在一个示例中,诸如社交网站或照片共享网站的网站允许用户上传照片,并且允许照片中出现的脸部被用户的姓名标记。 当用户A上传新照片时,可能会执行两次分析。 首先,将照片中的脸部与用户的已知脸部进行比较,以确定相似性。 其次,确定哪些其他用户A经常上传照片。 创建两个概率分布。 一个分配对于照片类似于新照片的用户分配高概率。 另一方则将频繁出现在用户A上传的照片中的用户分配给高概率。这些概率分布相结合,照片中的人被识别为具有最高概率的人。
    • 5. 发明申请
    • DETECTING VISUAL GESTURAL PATTERNS
    • 检测视觉图案
    • US20120159404A1
    • 2012-06-21
    • US13398645
    • 2012-02-16
    • Srinath VasireddySergey ChubGang HuaTing-yi Yang
    • Srinath VasireddySergey ChubGang HuaTing-yi Yang
    • G06F3/033
    • H04N19/533G06T7/223G06T2207/10016H04N19/56
    • A processing device and method are provided for capturing images, via an image-capturing component of a processing device, and determining a motion of the processing device. An adaptive search center technique may be employed to determine a search center with respect to multiple equal-sized regions of an image frame, based on previously estimated motion vectors. One of several fast block matching methods may be used, based on one or more conditions, to match a block of pixels of one image frame with a second block of pixels of a second image. Upon matching blocks of pixels, motion vectors of the multiple equal-sized regions may be estimated. The motion may be determined, based on the estimated motion vectors, and an associated action may be performed. Various embodiments may implement techniques to distinguish motion blur from de-focus blur and to determine a change in lighting condition.
    • 提供了一种处理装置和方法,用于经由处理装置的图像捕获部件捕获图像,并且确定处理装置的运动。 可以采用自适应搜索中心技术来基于先前估计的运动矢量来确定关于图像帧的多个等大小区域的搜索中心。 可以使用几种快速块匹配方法中的一种,基于一个或多个条件来匹配一个图像帧的像素块与第二图像的第二像素块。 在匹配像素块之后,可以估计多个等大小区域的运动矢量。 可以基于估计的运动矢量来确定运动,并且可以执行相关联的动作。 各种实施例可以实现将运动模糊与去焦点模糊区分开来并且确定照明条件的变化的技术。
    • 6. 发明授权
    • Face recognition using discriminatively trained orthogonal tensor projections
    • 使用区分训练正交张量投影的人脸识别
    • US07936906B2
    • 2011-05-03
    • US11763909
    • 2007-06-15
    • Gang HuaPaul A ViolaSteven M. DruckerMichael Revow
    • Gang HuaPaul A ViolaSteven M. DruckerMichael Revow
    • G06K9/00
    • G06K9/00288G06K9/6232
    • Systems and methods are described for face recognition using discriminatively trained orthogonal rank one tensor projections. In an exemplary system, images are treated as tensors, rather than as conventional vectors of pixels. During runtime, the system designs visual features—embodied as tensor projections—that minimize intraclass differences between instances of the same face while maximizing interclass differences between the face and faces of different people. Tensor projections are pursued sequentially over a training set of images and take the form of a rank one tensor, i.e., the outer product of a set of vectors. An exemplary technique ensures that the tensor projections are orthogonal to one another, thereby increasing ability to generalize and discriminate image features over conventional techniques. Orthogonality among tensor projections is maintained by iteratively solving an ortho-constrained eigenvalue problem in one dimension of a tensor while solving unconstrained eigenvalue problems in additional dimensions of the tensor.
    • 使用区分训练的正交秩一张量投影描述用于人脸识别的系统和方法。 在示例性系统中,图像被视为张量,而不是像传统的像素矢量。 在运行期间,系统设计视觉特征 - 体现为张量投影 - 最大限度地减少不同人脸部和脸部之间的类间差异,从而最大限度地减少同一脸部实例之间的差异。 张量投影在训练图像集上顺序追溯,并采取一级张量的形式,即一组向量的外积。 示例性技术确保张量投影彼此正交,从而增加了与常规技术相比的概括和区分图像特征的能力。 通过迭代求解张量的一维中的邻域约束特征值问题,同时解决张量的附加维度中的无约束特征值问题,维持张量投影中的正交性。
    • 7. 发明申请
    • COMPUTATIONALLY EFFICIENT LOCAL IMAGE DESCRIPTORS
    • 计算效率高的局部图像描述符
    • US20100246969A1
    • 2010-09-30
    • US12410469
    • 2009-03-25
    • Simon A. J. WinderGang Hua
    • Simon A. J. WinderGang Hua
    • G06K9/46
    • G06K9/4671
    • Described is a technology in which an image (or image patch) is processed into a highly discriminative and computationally efficient image descriptor that has a low storage footprint. Feature vectors are generated from an image (or image patch), and further processed via a polar Gaussian pooling approach (a DAISY configuration) into a descriptor. The descriptor is normalized, and processed with a dimension reduction component and a quantization component (based upon dynamic range reduction) into a finalized descriptor, which may be further compressed. The resulting descriptors have significantly reduced error rates and significantly smaller sizes than other image descriptors (such as SIFT-based descriptors).
    • 描述了一种技术,其中图像(或图像贴片)被处理成具有低存储空间的高度辨别性和计算效率的图像描述符。 特征向量从图像(或图像块)生成,并且通过极高斯混合方法(DAISY配置)进一步处理成描述符。 描述符被归一化,并且将尺寸减小分量和量化分量(基于动态范围缩小)处理成最终描述符,其可被进一步压缩。 所得到的描述符与其他图像描述符(例如基于SIFT的描述符)相比,显着降低了错误率和显着更小的大小。
    • 9. 发明授权
    • Flexible image comparison and face matching application
    • 灵活的图像比较和面部匹配应用
    • US08526684B2
    • 2013-09-03
    • US12637486
    • 2009-12-14
    • Amir AkbarzadehGang Hua
    • Amir AkbarzadehGang Hua
    • G06K9/00
    • G06K9/6212G06K9/00288
    • Two faces may be compared by calculating distances between different regions of the windows, and choosing one of the distances as the difference between the images. Two images are examined to detect the location of the face in the images. The faces may then be geometrically and photometrically rectified. A sliding window that is smaller than the whole face may be positioned at various locations over the images, and a descriptor is calculated for each window position. The descriptor for a window at one location in one image is compared with descriptors for windows in the neighborhood of that location in the other image. The lowest distance between window descriptors is chosen. The process is repeated for all window positions, resulting in a set of distances. The distances are sorted, and one of the distances is chosen to represent the difference between the two faces.
    • 可以通过计算窗口的不同区域之间的距离并选择其中一个距离作为图像之间的差异来比较两个面。 检查两个图像以检测图像中的脸部的位置。 然后可以几何和光学校正面。 小于整个脸部的滑动窗口可以位于图像上的各个位置,并且为每个窗口位置计算描述符。 将一个图像中一个位置的窗口的描述符与其他图像中该位置附近的窗口的描述符进行比较。 选择窗口描述符之间的最小距离。 对所有窗口位置重复该过程,产生一组距离。 距离被分类,并且选择一个距离来表示两个面之间的差异。
    • 10. 发明授权
    • Feature selection and extraction
    • 特征选择和提取
    • US08244044B2
    • 2012-08-14
    • US12109347
    • 2008-04-25
    • Gang HuaPaul ViolaDavid Liu
    • Gang HuaPaul ViolaDavid Liu
    • G06K9/62G06K9/46
    • G06K9/4647G06K9/468G06K9/6228
    • Image feature selection and extraction (e.g., for image classifier training) is accomplished in an integrated manner, such that higher-order features are merely developed from first-order features selected for image classification. That is, first-order image features are selected for image classification from an image feature pool, initially populated with pre-extracted first-order image features. The selected first-order classifying features are paired with previously selected first-order classifying features to generate higher-order features. The higher-order features are placed into the image feature pool as they are developed or “on-the-fly” (e.g., for use in image classifier training).
    • 图像特征选择和提取(例如,用于图像分类器训练)以集成的方式实现,使得仅从为图像分类选择的一阶特征开发高阶特征。 也就是说,从图像特征池中选择用于图像分类的一阶图像特征,最初用预提取的一阶图像特征填充。 所选择的一阶分类特征与先前选择的一阶分类特征配对以产生更高阶的特征。 更高阶的特征被放置在图像特征池中,因为它们被开发或“即时”(例如,用于图像分类器训练)。